Dynamics is fundamental for the function of proteins. Experimentally, protein dynamics can be studied in various types of nuclear magnetic resonance (NMR) experiments, but they give limited information about the detailed nature of the fluctuations. Molecular simulations, on the other hand, provides atomic resolution, but the possible length of the simulations is typically around a microsecond, and thus slower fluctuations, which are the key to most biological processes, will never be observed during the simulations.
In this project, we will use enhanced sampling methods to make slow fluctuations appear even in simulations of limited length. These methods artificially enhance the fluctuations along all or a few predefined degrees of freedom (usually denoted collective variables). Specifically, we will work within the metadynamics framework and develop a reliable and partly automatic protocol for selecting the best suitable collective variables to address various kinds of protein dynamics.
As part of this method development, we are applying the methods to well-defined problems, such as the nature of local unfolding events in BPTI and conformational gating in aspartic proteases, and difficult cases of protein-ligand binding.
In another but related project, we will try to understand dynamic communication pathways and transition states via local volume fluctuations. We have recently obtained unique results from NMR relaxation experiments that detect conformational transitions and communication pathways in proteins and also provide information on their transition states. However, a detailed, atomistic picture of the underlying protein dynamics cannot be reached from the experimental data alone. To reach this level, we need to interpret the experiments with the aid of advanced molecular dynamics simulations and related approaches. Thus, we will develop methods to leverage the experimental data and provide insights into protein dynamics underlying biological phenomena, e.g. allostery.
More specifically, we will develop computational tools to simulate large-scale fluctuations of protein structure, to characterize the resulting ensembles and calculate their properties, so that a proper comparison with experiment can be done and possible inconsistencies be addressed. Of particular interest are novel methods to:
(i) characterize the transition state ensemble for conformational transitions. Here, we focus on a seemingly trivial protein process, aromatic ring flipping, where experiments have indicated that the transition state has unique properties with a liquid-like compressibility, rather than the solid-like properties of the ground state.
(ii) delineate allosteric communication pathways through proteins. Here, we aim to map out networks of correlated volume fluctuations in the densely packed protein core, as a means to investigate communication pathways between remote sites in the protein, i.e. we aim to obtain an atomistic view of allosteric coupling between sites.
The key to both (i) and (ii) involves studying volume fluctuations and how these relate to conformational transitions. We believe that this project will significantly advance the state-of-the-art of the field, with broad impact in molecular life science.